Book Image

Azure Data Scientist Associate Certification Guide

By : Andreas Botsikas, Michael Hlobil
Book Image

Azure Data Scientist Associate Certification Guide

By: Andreas Botsikas, Michael Hlobil

Overview of this book

The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam.
Table of Contents (17 chapters)
Section 1: Starting your cloud-based data science journey
Section 2: No code data science experimentation
Section 3: Advanced data science tooling and capabilities

Configuring an AutoML experiment

If you were asked to train a model to make predictions against a dataset, you would need to do a couple of things, including normalizing the dataset, splitting it into train and validation data, running multiple experiments to understand which algorithm is performing best against the dataset, and then finetuning the best model. Automated machine learning shortens this process by fully automating the time-consuming, iterative tasks. It allows all users, from normal PC users to experienced data scientists, to build multiple machine learning models against a target dataset and select the model that performs the best, based on a metric you select.

This process consists of the following steps:

  1. Preparing the experiment: Select the dataset you are going to use for training, select the column that you are trying to predict, and configure the experiment's parameters. This is the configuration phase you will read about in this section.
  2. Data...